• Bulletin of the Korean Chemical Society
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• v.33 no.12
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• pp.4117-4121
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• 2012

The solvolysis rate constants of allyl chloroformate ($CH_2=CHCH_2OCOCl$, 3) in 30 different solvents are well correlated with the extended Grunwald-Winstein equation, using the $N_T$ solvent nucleophilicity scale and $Y_{Cl}$ solvent ionizing scale, with the sensitivity values of $0.93{\pm}0.05$ and $0.41{\pm}0.02$ for l and m, respectively. These l and m values can be considered to support a $S_N2$ reaction pathway. The activation enthalpies (${\Delta}H^{\neq}$) were 12.5 to 13.4 $kcal{\cdot}mol^{-1}$ and the activation entropies (${\Delta}S^{\neq}$) were -34.4 to -37.3 $cal{\cdot}mol^{-1}{\cdot}K^{-1}$, which is also consistent with the proposed bimolecular reaction mechanism. The solvent kinetic isotope effect (SKIE, $k_{MeOH}/k_{MeOD}$) of 2.16 was also in accord with the $S_N2$ mechanism. The values of product selectivity (S) for the solvolyses of 3 in alcohol/water mixtures was 1.3 to 3.9, which is also consistent with the proposed bimolecular reaction mechanism.

• Proceedings of the Korea Society of Design Studies Conference
• /
• 1995.11a
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• pp.60-61
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• 1995

• The Mathematical Education
• /
• v.12 no.2
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• pp.13-14
• /
• 1974

• The Mathematical Education
• /
• v.12 no.2
• /
• pp.15-17
• /
• 1974

• The Mathematical Education
• /
• v.12 no.2
• /
• pp.18-19
• /
• 1974

• The Mathematical Education
• /
• v.12 no.2
• /
• pp.20-22
• /
• 1974

• East Asian mathematical journal
• /
• v.12 no.2
• /
• pp.203-211
• /
• 1996

• The Mathematical Education
• /
• v.15 no.1
• /
• pp.12-14
• /
• 1976

• The Mathematical Education
• /
• v.10 no.2
• /
• pp.12-21
• /
• 1972

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.989-995
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• 2017

As is known to all that the output of wind power generation has a character of randomness and volatility because of the influence of natural environment conditions. At present, the research of wind power prediction mainly focuses on point forecasting, which can hardly describe its uncertainty, leading to the fact that its application in practice is low. In this paper, a wind power range prediction model based on the multiple output property of BP neural network is built, and the optimization criterion considering the information of predicted intervals is proposed. Then, improved Particle Swarm Optimization (PSO) algorithm is used to optimize the model. The simulation results of a practical example show that the proposed wind power range prediction model can effectively forecast the output power interval, and provide power grid dispatcher with decision.